.png){kind=icon}
.png){kind=icon}
Return
Engineering Algorithms for Scalability through Continuous Validation of Performance Expectations
.png){kind=icon}
OA
AI
DOI：10.1109/TPDS.2019.2896993.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Many libraries in the HPC field use sophisticated algorithms with clear theoretical scalability expectations. However, hardware constraints or programming bugs may sometimes render these expectations inaccurate or even plainly wrong. While algorithm and performance engineers have already been advocating the systematic combination of analytical performance models with practical measurements for a very long time, we go one step further and show how this comparison can become part of automated testing procedures. The most important applications of our method include initial validation, regression testing, and benchmarking to compare implementation and platform alternatives. Advancing the concept of performance assertions, we verify asymptotic scaling trends rather than precise analytical expressions, relieving the developer from the burden of having to specify and maintain very fine-grained and potentially non-portable expectations. In this way, scalability validation can be continuously applied throughout the whole development cycle with very little effort. Using MPI and parallel sorting algorithms as examples, we show how our method can help uncover non-obvious limitations of both libraries and underlying platforms.
Keywords:
Software engineering
high performance computing
parallel programming
performance analysis
performance modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
5.2K
Citations:
1.1W
View more organizations